DETAILED ACTION
This action is in response to communications files on 12/29/2025. Claims 1-12 were amended, claims 13-14 were cancelled, no new claims were added. Claims 1-12 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The applicant states that support for the amendments to the claims can be found throughout the specification and the drawings originally filed but does not disclose particular citations to where support may be found with regard for the amendments.
The examiner has evaluated the originally filed disclosure to identify support for the amended matter. Support for various limitations amended into the claims was found in at least the corresponding paragraphs of the originally filed specification:
a computer configured to [0027]
prediction target battery [0025]
sample batteries of the same type as the prediction target battery [0038]
highest prediction accuracy [0046]
integral values of the product of the charge current of the battery and the time [0033]
integral values of time spent within predetermined voltage ranges [0031]
integral values of time spent within predetermined temperature ranges [0032]
The amendments to the claims appear to be adequately supported by the originally filed disclosure such that it is apparent to the examiner that the applicant had possession of the claimed invention at the time of filing. No new matter has been introduced by way of amendment to the claims.
Response to Arguments
Claim objections
The applicant has cancelled claim 13 in response to the previously set forth objection for being a substantial duplicate of claim 7.
Accordingly, the objection is withdrawn by way of this cancellation.
Claim rejections under 35 U.S.C. § 112
The applicant has amended the claims such that they recite the structure of a computer within a battery degradation prediction device to perform the claimed functions. Accordingly, the claims are no longer being interpreted under 35 U.S.C. § 112(f). Inclusion of such structure of a computer configured to perform the functions provides adequate written description. The rejection under 35 U.S.C. § 112(a) has been withdrawn.
Likewise, because the applicant has amended the claim such that the claim limitation is no longer interpreted as a limitation under 35 U.S.C. § 112(f), the claims now recite the corresponding structure linked to the function such that one having ordinary skill in the art would recognize what structure performs the claimed functions. The rejections under 35 U.S.C. § 112(b) have been withdrawn.
Claim rejections under 35 U.S.C. § 103
Applicant has amended the claims and argues that the prior art of record does not disclose the claims as amended for various reasons.
Applicant’s arguments with respect to claims 1-14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim rejections under 35 U.S.C. § 101
Applicant argues that the battery degradation prediction device of claim 1 enables accurate prediction of the degradation of the prediction target by calculating the predicted value of the degradation indicator for the prediction target batter under the optimal exponent with highest prediction accuracy. The applicant alleges that this optimization is not a mental process or a generic mathematical calculation but rather a specific, data-driven technique that improves the functioning of a battery management system.
Applicants arguments have been considered but are not persuasive. Making predictions and performing calculations are mental processes and mathematical concepts, respectively. The inventive concept of the instant claims appears to be rooted in the steps which can be construed as the judicial exceptions themselves. Per MPEP 2106.05, the abstract idea alone cannot provide the inventive concept. Rather, the inventive concept must be furnished by an additional element in the claim that integrates the judicial exception(s) into a practical application or amounts to significantly more than the judicial exception. There are no such elements identified within the claims which would do so. With respect to the argument that the technique claimed improves the functioning of a battery management system, the battery management system, under broadest reasonable interpretation, is a generic computer. The computer appears to be functioning in its ordinary capacity and is only used as a tool to execute the recited judicial exceptions. The functioning of the computer itself is not improved by the calculations performed by it.
Applicant further argues that the battery degradation prediction device of claim 8 enables accurate prediction of the degradation of the prediction target battery by calculating the predicted value of the degradation indicator for the prediction target battery using the linear function of the power on a plurality of parameters. Applicant particularly argues that any alleged abstract ideas would be integrated into a practical application because of the accurate prediction which enables enhanced battery management.
Applicant arguments have been considered but are not persuasive. The utilization of the linear function is the recitation of mathematical concepts (the judicial exception) which is subsequently used to make a predicted value of the degradation indicator, which is a further recitation of a mental process since a human being can derive evaluation and judgments in their mind or using pen and paper as assistive physical aids. The battery degradation prediction device is understood to be a generic computer. Stating that a generic computer enables accurate prediction is simply using a computer as a tool to perform the mental process and does not impose meaningful limits on the claims. There are no limitations in the claims that reflect enhanced battery management practices that would integrate the exceptions into a practical application.
For the reasons stated in this response, in conjunction with the updated rejection of this action, the claims remain rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following section follows the 2019 Patent Eligibility Guidance (PEG) for analyzing subject matter eligibility:
Step 1 - Statutory Category:
Step 1 of the PEG analysis entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101 (process, machine, manufacture, or composition of matter).
Step 2A Prong 1 - Judicial exception:
In Step 2A Prong 1, examiners evaluate whether the claim recites a judicial exception (an abstract idea, law of nature, or a natural phenomenon).
Step 2a Prong 2 - Integration into a practical application:
If claims recite a judicial exception, the claim requires further analysis in Step 2A Prong 2. In Step 2A Prong 2, examiners evaluate whether the claim as a whole integrates the exception into a practical application.
Step 2B - Significantly More:
If the additional elements identified in Step 2A Prong 2 do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception and requires further analysis under Step 2B- Significantly More.
As noted in the MPEP 2106.05(II): The identification of the additional element(s) in the claim from Step 2A Prong 2, as well as the conclusions from Step 2A Prong 2 on the considerations discussed in MPEP 2106.05(a) -(c), (e), (f), and (h) are to be carried over. Claim limitations identified as Insignificant Extra-Solution Activities are further evaluated to determine if the elements are beyond what is well -understood, routine, and conventional (WURC) activity, as dictated by MPEP 2106.05(II).
Independent Claims:
Claim 1:
Step 1: Claim 1 and its dependent claims 2-7 are directed to a device which falls within one of the four statutory categories of a machine.
Step 2A Prong 1: Claim 1 recites a judicial exception, noted in bold:
calculate the usage history parameters on a basis of the time series data; -This claim limitation can reasonably be read to entail evaluating time series data so as to derive usage history parameters. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the recitation of “calculate” is the recitation of a mathematical calculation. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
generate input parameters by raising the usage history parameters by an optimal exponent: and- This claim limitation can be reasonably read to entail evaluating a usage history parameter with regard to an exponent value so as to determine parameter values. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. This is supported by the specification, [0040] which describes a designer (understood to be a human being) performing the task of raising the plurality of usage parameters by a prescribed exponent. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Further, the recitation of “raising the usage history parameters by an optimal exponent” is the recitation of a mathematical calculation. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
calculate the predicted value of the degradation indicator for the prediction target battery by inputting the input parameters into the battery model as explanatory variables, wherein a value of the optimal exponent is set by a method comprising steps of: -This claim limitation can be reasonably read to entail evaluating input parameter values with regard for a battery model in terms of variables. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Additionally, the recitation of “calculating a predicted value” [[using]] “explanatory variables” is the explicit recitation of mathematical calculations. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
(B) raising the usage history parameters by a prescribed exponent to thereby generate time series data of input parameters; The claim limitation can be reasonably read to entail exponentiating parameters to generate data. This is the explicit recitation of a mathematical calculation (raising parameters by a prescribed exponent). Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept. Furthermore, this task can be performed within the human mind or using a pen and paper as an assistive physical aid. For example, a human can take a parameter value and apply an exponentiation to the parameter value with consideration to a time to generate a time series data result. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
(D) by repeatedly performing steps (B) and (C) while varying a value of the exponent, determining a value of the optimal exponent with highest prediction accuracy The claim limitation can be reasonably read to entail performing the exponentiating step and the training step to evaluate an exponent for optimality in the application. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, and in this particular case, utilizes the training of a machine learning algorithm (as a generic computing component), to execute the mental process. The courts do not distinguish between mental processes performed in the human mind or on a computer and therefore the claim recites the judicial exception of abstract ideas as a mental process. Furthermore, this claim recites the evaluation of a value with regard to a highest accuracy number which is the recitation of a mathematical relationship. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
a computer configured to: - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of a computer to perform the judicial exception
acquire time series data about a current, a voltage, and a temperature of a prediction target battery; - This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering.
(A) acquiring time series data about the usage history parameters and the degradation indicator for a plurality of sample batteries of the same type as the prediction target battery; - This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Further, the claim has been identified as generally linking the use of the judicial exception to a particular technological environment Field of Use and Technological Environment (MPEP 2106.05(h))
(C) training the battery model by using the time series data of the input parameters and the degradation indicator as training data; and– This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation suggests mere instructions to implement the abstract idea on a computer or merely use a computer model in its ordinary capacity as a tool to execute the judicial exception.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))); and generally linking the use of the judicial exception to a particular technological environment and field of use (Field of Use and Technological Environment (MPEP 2106.05(h))) does not integrate the judicial exception into a practical application.
The additional elements do not appear to integrate the judicial exception into a practical application.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
acquire time series data about a current, a voltage, and a temperature of a prediction target battery; – This limitation has been identified as the insignificant extra solution activity of mere data gathering, as stated previously. Under broadest reasonable interpretation and when read in light of the specification, the limitation includes receiving data over a network. The courts have found that receiving and transmitting data over a network are computer functions that are well-understood, routine, and conventional.
(A) acquiring time series data about the usage history parameters and the degradation indicator for a plurality of sample batteries of the same type as the prediction target battery: – This limitation has been identified as the insignificant extra solution activity of mere data gathering, as stated previously. Under broadest reasonable interpretation and when read in light of the specification, the limitation includes receiving data over a network. The courts have found that receiving and transmitting data over a network are computer functions that are well-understood, routine, and conventional.
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), as stated previously. The courts have found that merely using a computer as a tool to perform a mental process and generally linking the use of the judicial exception to a particular technological environment and field of use does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Claim 8:
Step 1: Claim 8 and its dependent claims 9-12 are directed to a device which falls within one of the four statutory categories of a machine.
Step 2A Prong 1: Claim 8 recites a judicial exception, noted in bold:
calculate the usage history parameters on a basis of the time series data; -This claim limitation can reasonably be read to entail evaluating time series data so as to derive usage history parameters. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the recitation of “calculate” is the recitation of a mathematical calculation. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
generate input parameters by raising the usage history parameters by an optimal exponent; and- This claim limitation can be reasonably read to entail evaluating a usage history parameter with regard to an exponent value so as to determine parameter values. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. This is supported by the specification, [0040] which describes a designer (understood to be a human being) performing the task of raising the plurality of usage parameters by a prescribed exponent. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Further, the recitation of “raising the usage history parameters by an optimal exponent” is the recitation of a mathematical calculation. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
calculate the predicted value of the degradation indicator for the prediction target battery by inputting the input parameters into the battery model as explanatory variables, wherein- This claim limitation can be reasonably read to entail evaluating input parameter values with regard for a battery model in terms of variables. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Additionally, the recitation of “calculating a predicted value” [[using]] “explanatory variables” is the explicit recitation of mathematical calculations. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
the battery model is a linear regression model expressing the objective variable as a linear function of a plurality of the explanatory variables, and The claim limitation can be reasonably read to entail describing a model in terms of its mathematical function and variables within the function as a mathematical relationship. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
a computer configured to: - This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of a computer to perform the judicial exception
acquire time series data about a current, a voltage, and a temperature of a prediction target battery; - This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering.
the usage history parameters include current factor parameters that treat the current of the battery as a factor, voltage factor parameters that treat the voltage of the battery as a factor, and temperature factor parameters that treat the temperature of the battery as a factor -This claim limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for generally linking the use of the judicial exception to a particular technological environment or field of use.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))); and generally linking the use of the judicial exception to a particular technological environment and field of use (Field of Use and Technological Environment (MPEP 2106.05(h))) does not integrate the judicial exception into a practical application.
The additional elements do not appear to integrate the judicial exception into a practical application.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
acquire time series data about a current, a voltage, and a temperature of a prediction target battery; – This limitation has been identified as the insignificant extra solution activity of mere data gathering, as stated previously. Under broadest reasonable interpretation and when read in light of the specification, the limitation includes receiving data over a network. The courts have found that receiving and transmitting data over a network are computer functions that are well-understood, routine, and conventional.
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), as stated previously. The courts have found that merely using a computer as a tool to perform a mental process and generally linking the use of the judicial exception to a particular technological environment and field of use does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Dependent Claims:
Examiner notes limitations identified as judicial exceptions are indicated in italicized bold and limitations identified as additional elements are indicated using italics.
Claim 2
Step 1: Regarding dependent claim 2, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 2 additionally recites the limitation wherein the battery model is a linear regression model expressing the objective variable as a linear function of the explanatory variables, which can reasonably be read to describe the battery model in terms of the mathematical relationships that define it. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
Step 2A Prong 2 & Step 2B: Claim 2 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 3
Step 1: Regarding dependent claim 3, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 3 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 3 additionally recites the limitation wherein the usage history parameters include current factor parameters that treat the current of the battery as a factor, voltage factor parameters that treat the voltage of the battery as a factor, and temperature factor parameters that treat the temperature of the battery as a factor. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the limitation generally links the use of the judicial exception to battery degradation. The courts have ruled generally linking the use of a judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of the judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 4
Step 1: Regarding dependent claim 4, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 4 additionally recites the limitation wherein the step (D) comprises determining the optimal exponent with a common value for the current factor parameters, the voltage factor parameters, and the temperature factor parameters, which further describes the mental process recited in claim 1, wherein a human is capable of performing a search for an optimal exponent and can furthermore evaluate the optimal exponent with regard to the factor parameters as a whole. Therefore, this claim further recites details of the mental process. The recitation of the word “common” with regard to the exponent value describes a mathematical relationship wherein an exponent value is applied to multiple variables. Therefore, this claim also includes the recitation of judicial exception of abstract ideas of mathematical concepts.
Step 2A Prong 2 & Step 2B: Claim 4 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more than the judicial exceptions.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 5
Step 1: Regarding dependent claim 5, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 5 additionally recites the limitation wherein the step (D) comprises determining the optimal exponent with independent values for the current factor parameters, the voltage factor parameters, and the temperature factor parameters, further describes the mental process recited in claim 1, wherein a human is capable of performing a search for an optimal exponent and can furthermore evaluate the optimal exponents with regard to the factor parameters individually/independently. Therefore, this claim further recites details of the mental process. The recitation of the word “independent” with regard to the exponent value describes a mathematical relationship wherein an exponent value is applied to multiple variables. Therefore, this claim also includes the recitation of judicial exception of abstract ideas of mathematical concepts.
Step 2A Prong 2 & Step 2B: Claim 5 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more than the judicial exceptions.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 6
Step 1: Regarding dependent claim 6, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 6 additionally recites the limitation wherein the step (D) comprises determining a value for the optimal exponent in a range from 0 to 1 which further describes the mental process recited in claim 1 and additionally recites the evaluation of the optimal exponent that falls within a range, which is a mathematical relationship of numbers. Therefore, in addition to this limitation further reciting details of the mental process, the claim further recites a mathematical relationship which is indicative of reciting the judicial exception of abstract ideas as a mathematical concept.
Step 2A Prong 2 & Step 2B: Claim 6 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 7
Step 1: Regarding dependent claim 7, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 7 additionally recites the limitation and the optimal exponent is found by evaluating a prediction accuracy of the battery model trained using the verification data., which can reasonably be read to entail evaluating a prediction accuracy of the battery model. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, a prediction accuracy is a mathematical calculation and therefore this claim additionally recites the judicial exception of abstract ideas of mathematical concepts.
Step 2A Prong 2: Claim 7 additionally recites the limitations wherein in the step (C), a portion of the time series data of the input parameters and the degradation indicator that belongs to a prescribed training period is treated as the training data, and and in the step (D), a portion of the time series data of the input parameters and the degradation indicator that belongs to a verification period subsequent to the training period is treated as verification data. These limitations have been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the limitation generally links the judicial exception to the particular technological environment of using a model that has training and testing data with the recited particularities. The courts have ruled generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of the judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 9
Step 1: Regarding dependent claim 9, the judicial exception of independent claim 8 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1 Claim 9 additionally recites wherein the current factor parameters are integral values of product of the current of the battery and time, voltage factor parameters are integral values of time spent within predetermined voltage ranges and temperature factor parameters are integral values of time spent within predetermined temperature ranges. -This limitation can reasonably be read to entail defining factor parameters by their mathematical descriptions (integrals, product, etc.) which are mathematical relationships and calculations to describe the values. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
Step 2A Prong 2 & Step 2B: Claim 9 does not recite any additional elements that would integrate the judicial exceptions into a practical application nor amount to significantly more than the recited judicial exceptions.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 10
Step 1: Regarding dependent claim 10, the judicial exception of independent claim 8 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 10 additionally recites the limitation generate input parameters by raising the current factor parameters, the voltage factor parameters, and the temperature factor parameters by the optimal exponent set to a common value for each, which further describes the mental process recited in claim 8, wherein a human is capable of evaluating parameter values with respect to an exponent value. Therefore, this claim further recites details of the mental process. The recitation of the word “common” with regard to the exponent value describes a mathematical relationship wherein an exponent value is applied to multiple variables. Therefore, this claim also includes the recitation of judicial exception of abstract ideas of mathematical concepts.
Step 2A Prong 2: Claim 10 additionally recites the limitation wherein the computer is further configured to which has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)). The courts have found that using a computer as a tool to perform an existing process does not integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to invoking the use of generic computers to perform the judicial exception are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 11
Step 1: Regarding dependent claim 11, the judicial exception of independent claim 8 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 11 additionally recites the limitation generate input parameters by raising the current factor parameters, the voltage factor parameters, and the temperature factor parameters by the optimal exponent set to independent values for each further describes the mental process recited in claim 8, wherein a human is capable of evaluating parameter values with respect to an exponent value. Therefore, this claim further recites details of the mental process. The recitation of the word “independent” with regard to the exponent value describes a mathematical relationship wherein an exponent value is applied to multiple variables. Therefore, this claim also includes the recitation of judicial exception of abstract ideas of mathematical concepts.
Step 2A Prong 2: Claim 11 additionally recites the limitation wherein the computer is further configured to which has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)). The courts have found that using a computer as a tool to perform an existing process does not integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to invoking the use of generic computers to perform the judicial exception are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 12
Step 1: Regarding dependent claim 12, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 12 additionally recites the limitation wherein a value of the optimal exponent is set within a range from 0 to 1 which further describes the mental process recited in claim 1 because a human being can make a judgement as to a value with regard for a specified range. The claim limitation additionally recites the evaluation of the optimal exponent that falls within a range, which is a mathematical relationship of numbers. Therefore, in addition to this limitation further reciting details of the mental process, the claim further recites a mathematical relationship which is indicative of reciting the judicial exception of abstract ideas as a mathematical concept.
Step 2A Prong 2 & Step 2B: Claim 12 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6, and 7 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mizoguchi et al (US 2023/0003809 A1 with priority date 12/06/2019), hereinafter referred to as Mizoguchi.
Regarding claim 1, Mizoguchi discloses A battery degradation prediction device that calculates a predicted value of a degradation indicator for a battery according to a battery model that treats powers of a plurality of usage history parameters defined on a basis of time series data about a current, a voltage, and a temperature of a battery as explanatory variables and treats a predicted value of a degradation indicator for the battery as an objective variable, the battery degradation prediction device comprising: A deterioration estimation device is disclosed that outputs the estimated life of a lead acid battery ((Mizoguchi, ¶75) " the deterioration estimation device described above, in a case where a current or a voltage when discharging is performed until the lead-acid battery or the lead-acid battery module reaches a predetermined SOC is inputted to the deterioration estimation device, the first estimation unit may input the acquired current or voltage to a learning model that outputs the rate of deterioration so as to estimate the rate of deterioration of the lead-acid battery or the lead-acid battery module."). The estimation device leverages a learning model to intake temperature, current, and voltage data to estimate the state of health of the battery ((Mizoguchi, ¶196) " The input data may include, besides the internal resistance, at least one of an internal resistance in a fully charged state, an open circuit voltage, a discharge capacity, a discharge voltage (an estimated value of the discharge capacity based on the discharge voltage), and a temperature obtained by a temperature sensor 7."). The parameters are fitted to an exponential curve to estimate the behavior of the battery (See Fig 2) and ((Mizoguchi, ¶289-290) " The control unit 11 estimates a time-series transition of the internal resistance (internal resistance curve), and stores the estimated transition of the internal resistance in the relationship DB 144 (S185). The control unit 11 derives an internal resistance curve using a method such as linear approximation or curve approximation based on the internal resistance derived this time and a plurality of internal resistances derived in the past. The past internal resistance curves may be stored in the relationship DB 144 based on the data of the deterioration history DB 142, and the internal resistance curve in the current estimation may be derived with reference to the past internal resistance curves. The control unit 11 estimates a time-series transition of the internal resistance (internal resistance curve), and stores the estimated transition of the internal resistance in the relationship DB 144 (S185). The control unit 11 derives an internal resistance curve using a method such as linear approximation or curve approximation based on the internal resistance derived this time and a plurality of internal resistances derived in the past. The past internal resistance curves may be stored in the relationship DB 144 based on the data of the deterioration history DB 142, and the internal resistance curve in the current estimation may be derived with reference to the past internal resistance curves. deterioration curve in the use history DB 143 (S186). The control unit 11 estimates the second degree-of-deterioration curve based on the internal resistance curve estimated this time and the first degree-of-deterioration curve (the relationship between the degree of deterioration and the internal resistance) stored in the relationship DB 144. ")
a computer configured to: ((Mizoguchi, ¶97) " The control units 11 may be constituted of a central processing unit (CPU), a read only memory (ROM), a random access memory (RAM), and the like. The control unit 11 may include a graphics processing unit (GPU). The control unit 11 may use a quantum computer. ")
acquire time series data about a current, a voltage, and a temperature of a prediction target battery; ((Mizoguchi, ¶170) "The control unit 21 acquires a current and a voltage from the history data 24, and transmits the acquired current and voltage to the deterioration estimation device 1 (S222)."); ((Mizoguchi, ¶240) "The control unit 21 acquires a current, a voltage, a SOC and a temperature from the history data 24, and transmits the acquired current, voltage, SOC and temperature to the deterioration estimation device 1 (S252).")
calculate the usage history parameters on a basis of the time series data; Internal resistance is calculated and stored in a use history database ((Mizoguchi, ¶172) "The control unit 11 derives the internal resistance, and stores the internal resistance in the use history DB 143 (S123).") See figure 20 depicting that the derivation of internal resistance relies upon the received current and voltage data, thereby indicating that the internal resistance is determined on the basis of such historical data. Internal resistance is also described as being derived using equations 1-3, which leverage voltage values at different time periods. ((Mizoguchi, ¶51) " The first internal resistance R is derived from the following equation (I) in a case where the first internal resistance R is paused after discharging. equation (1) "); ((Mizoguchi, ¶55) " The second internal resistance R is derived from the following equation (2) in a case where the battery is charged after a pause. equation (2) "); ((Mizoguchi, ¶60) " A point of time that discharging finishes immediately before charging starts. Accordingly, the internal resistance is calculated by the same equation as the first internal resistance or the second internal resistance. That is, in this case, the internal resistance R is derived by the following equation (3). equation (3) "). The use history database contains data for each of a plurality of state of charge values (SOC) ((Mizoguchi, ¶107) "The use history DB 143 stores a number column, an internal resistance column consisting of a first internal resistance column, a second internal resistance column, and a third internal resistance column, and a degree-of-deterioration column for each of a plurality of SOC ( estimated SOC) for each battery 3.")
generate input parameters by raising the usage history parameters by an optimal exponent; and Derived internal resistance (which are stored in the usage history database as stated previously) is used with a first degree of deterioration curve to acquire a plurality of degrees of deterioration. The degrees of deterioration are used as input to the learning model, thereby indicating that this data is being used as input parameters. The degree of deterioration curve is selected based off the correlation to the derived internal resistance value, as an optimal match to the measurements of the controlled battery ((Mizoguchi, ¶257-260) "The control unit 11 estimates the degree of deterioration based on the derived internal resistance, and stores the degree of deterioration in the use history DB 143 (S164). The control unit 11 reads the relationship DB 144, and estimates the degree of deterioration with reference to the first degree-of-deterioration curve based on the derived internal resistance. The control unit 11 acquires a plurality of degrees of deterioration (S165). The control unit 11 inputs a plurality of degrees of deterioration in time series to the learned learning model 148 and acquires a plurality of future degrees of deterioration (S166). The control unit 11 estimates the transition of the degrees of deterioration in time series (second degree-of-deterioration curve) as described above based on the plurality of degrees of deterioration in the past, at the present, and in the future, and stores the estimated transition of the degrees of deterioration in the relationship DB 144 (S167)."). The first degree of deterioration curve is depicted as an exponential curve ((Mizoguchi, ¶17) "FIG. 2 is a graph illustrating an example of a first degree-of-deterioration curve."). The internal resistance curve can be quantified as a transition value that characterizes the curve, which would be understood to be an exponent value based on the curve’s behavior ((Mizoguchi, ¶174) "The control unit 11 estimates a time-series transition of the internal resistance (internal resistance curve), and stores the estimated transition in the relationship DB 144 (S125)."). To determine future degrees of deterioration, the input values would be mathematically correlated to continue to the trajectory of the degree of deterioration and therefore the input parameters would be understood to be raised by the exponent that characterizes the behavior so as to produce the output.
calculate the predicted value of the degradation indicator for the prediction target battery by inputting the input parameters into the battery model as explanatory variables, The plurality of degrees of deterioration, derived as input parameters as stated above, are input into a learning model characterizing behavior of the battery, wherein input data to a neural network is understood to be the explanatory variables that dictate the output of the model. The learning model is further used to estimate a second degree of deterioration curve that is used to estimate the life of the battery which is then displayed on the display panel of the battery control device as the indicator of degradation ((Mizoguchi, ¶259-263) " The control unit 11 inputs a plurality of degrees of deterioration in time series to the learned learning model 148 and acquires a plurality of future degrees of deterioration (S166). The control unit 11 estimates the transition of the degrees of deterioration in time series (second degree-of-deterioration curve) as described above based on the plurality of degrees of deterioration in the past, at the present, and in the future, and stores the estimated transition of the degrees of deterioration in the relationship DB 144 (S167). The control unit 11 estimates the life (S168). The control unit 11 acquires the time ta when the degree of deterioration becomes a threshold a as the life (an exchange time). The control unit 11 transmits the second degree-of- deterioration curve and the life to the battery control device 2 (S169), and finishes the processing. The control unit 21 receives the second degree-of-deterioration curve and the life (S263), displays the second degree-of-deterioration curve and the life on the display panel 25 (S264), and finishes the processing. ") wherein a value of the optimal exponent is set by a method comprising steps of: A learning model is used to determine the optimal rate of deterioration, wherein such learning model is generated accordingly: ((Mizoguchi, ¶192) " The learning model DB 145 store learning models 146 generated for a plurality of respective reached SOCs (estimated SOCs). "); ((Mizoguchi, ¶219-220) " The control unit 11 estimates a numerical value of the rate of deterioration having the highest probability among the degrees of deterioration that the learning model 146 outputs as a rate of deterioration at the current estimation time (S145), and finishes the processing ")
acquiring time series data about the usage history parameters and the degradation indicator for a plurality of sample batteries of the same type as the prediction target battery: ((Mizoguchi, ¶102) " The deterioration history DB 142 stores a number column, an internal resistance column consisting of a first internal resistance column, a second internal resistance column, and a third internal resistance column, and a degree-of- deterioration column for each of a plurality of reached SOC ( estimated SOC). The number column stores: the numbers of rows in a case where the determination of the deteriorations of the batteries 3 is performed with respect to a plurality of different batteries 3 or at different timings of the same batteries 3. ")
raising the usage history parameters by a prescribed exponent to thereby generate time series data of input parameters; Time series data corresponding to internal resistance is derived for a plurality of future points in time and this data is used as input at the input layer ((Mizoguchi, ¶267-268) "The plurality of internal resistances in time series mean a plurality of internal resistances in time series from the past to the current estimation point of time in the same battery 3. The internal resistance is derived from a current and a voltage. The degrees of deterioration at a plurality of future points of time mean the degrees of deterioration at a plurality of future points of time such as the next highest point of time and the next highest point of time after the next highest point of time with respect to the current estimation point of time. The input layer includes a single neuron or a plurality of neurons that receive a plurality of internal resistances in time series, and passes the inputted respective internal resistances to the intermediate layer."). The internal resistance are stored in the use history database, thereby indicating that the internal resistance is a usage history parameter ((Mizoguchi, ¶172) " The control unit 11 derives the internal resistance, and stores the internal resistance in the use history DB 143 (S123)."). The time series data for the future values of internal resistance are estimated according to the previous time points that correspond to the trajectory of an estimated internal resistance curve in the relationship database (as a prescribed curve) ((Mizoguchi, ¶174-175) " The control unit 11 estimates a time-series transition of the internal resistance (internal resistance curve), and stores the estimated transition in the relationship DB 144 (S125). The control unit 11 derives an internal resistance curve using a method such as curve approximation based on the internal resistance derived this time and a plurality of internal resistances derived in the past. The past internal resistance curves may be stored in the relationship DB 144 based on the data of the deterioration history DB 142, and the internal resistance curve in the current estimation may be derived with reference to the past internal resistance curves. As illustrated in FIG. 9, the internal resistances estimated at the current estimated point of time t, at the previous estimated point of time t-1, and at the estimation point of time t-2 that is the second most recent point of time are plotted to estimate a future internal resistance curve.") The degree of deterioration follows an exponential pattern, as can be seen in Figure 2.
PNG
media_image1.png
604
703
media_image1.png
Greyscale
The internal resistance curve can be quantified as a transition value that characterizes the curve, which would be understood to be an exponent value based on the curve’s behavior ((Mizoguchi, ¶174) "The control unit 11 estimates a time-series transition of the internal resistance (internal resistance curve), and stores the estimated transition in the relationship DB 144 (S125).")
training the battery model by using the time series data of the input parameters and the degradation indicator as training data; and Teacher data is used to train (learn) the learning model that characterizes the battery behavior. ((Mizoguchi, ¶209) "Using the teacher data, the control unit 11 generates the learning model 146 (learned type) that outputs the probability of the rate of deterioration when the internal resistance is inputted (S302). Specifically, the control unit 11 inputs teacher data to the input layer, performs arithmetic processing in the intermediate layer, and acquires the probability of rate of deterioration from the output layer."). The teacher data is described as including time series data of internal resistance as problem data and degrees of deterioration are used as answer data ((Mizoguchi, ¶266) " A neural network in which the plurality of internal resistances in the time series are inputted and the degrees of deterioration in the plurality of points of time in the future are outputted is constructed by performing learning based on teacher data in which internal resistances at a plurality of points of time in the time series are used as problem data and the degrees of deterioration in the plurality of points of time in the future are used as answer data.").
by repeatedly performing steps (B) and (C) while varying a value of the exponent, determining a value of the optimal exponent with highest prediction accuracy. The estimated rate of deterioration in the learning model is optimized so as to be in agreeance with the measured rate (as accuracy) ((Mizoguchi, ¶224) " The control unit enables relearning of the learning model 146 based on the rate of deterioration estimated using the learning model 146 and the rate of deterioration obtained by actual measurement so that the reliability of the estimation of the rate of deterioration is improved. The actually measured degree of deterioration is obtained in the predetermined row of the use history DB 35, and when the estimated rate of deterioration and the rate of deterioration based on the actually measured degree of deterioration agree with each other, relearning is performed by inputting a large number of teacher data in which the rate of deterioration is associated with the internal resistance in the row so that the probability of the rate of deterioration can be improved. When the estimated rate of deterioration and the actually measured rate of deterioration do not agree with each other, relearning is performed by inputting the teacher data where the actually measured rate of deterioration is associated with the internal resistance. "). The degree of deterioration follows an exponential pattern, as can be seen in Figure 2.
PNG
media_image1.png
604
703
media_image1.png
Greyscale
The internal resistance curve can be quantified as a transition value that characterizes the curve, which would be understood to be an exponent value based on the curve’s behavior ((Mizoguchi, ¶174) "The control unit 11 estimates a time-series transition of the internal resistance (internal resistance curve), and stores the estimated transition in the relationship DB 144 (S125).")
Regarding claim 6, Mizoguchi discloses The battery degradation prediction device according to claim 1, as stated previously. Mizoguchi discloses generating input parameters according to an exponential curve when considering internal resistance, as described in the rejection of claim 1, which would be contrary to containing an exponent in a range from 0 to 1. However, Mizoguchi alternatively describes using conductance values in determining the rate of deterioration. wherein the step (D) comprises determining a value for the optimal exponent in a range from 0 to 1. Mizoguchi explicitly states that the conductance is the reciprocal of the resistance ((Mizoguchi, ¶72) " The rate of deterioration can also be estimated using conductance that is a reciprocal of resistance and is measured by a battery tester or the like."). Accordingly, by applying the reciprocal values of the internal resistance in the plot of Figure 2 as opposed to the internal resistance, one having skill in the art would expect to see a generally exponentially decaying plot, wherein a decay function is understood to be characterized by an exponent between the values of 0 and 1.
PNG
media_image1.png
604
703
media_image1.png
Greyscale
Regarding claim 7, Mizoguchi discloses The battery degradation prediction device according to claim 1, as stated previously. Mizoguchi further discloses wherein in the step (C), a portion of the time series data of the input parameters and the degradation indicator that belongs to a prescribed training period is treated as the training data, and Input data is distinguished as data for learning (training) and includes internal resistance as time series data, as stated previously ((Mizoguchi, ¶196) " A vector having the same number of components as the number of nodes in the input layer is given as input data to the learning model 146 (input data for learning and input data for estimation). The learned input data includes at least the internal resistance at the reached SOC ( estimated SOC). The input data may include, besides the internal resistance, at least one of an internal resistance in a fully charged state, an open circuit voltage, a discharge capacity, a discharge voltage (an estimated value of the discharge capacity based on the discharge voltage), and a temperature obtained by a temperature sensor 7."). The model can be re-trained using the internal resistance values (input parameters) and the corresponding rate of deterioration (degradation indicator) ((Mizoguchi, ¶224) "The control unit enables relearning of the learning model 146 based on the rate of deterioration estimated using the learning model 146 and the rate of deterioration obtained by actual measurement so that the reliability of the estimation of the rate of deterioration is improved. The actually measured degree of deterioration is obtained in the predetermined row of the use history DB 35, and when the estimated rate of deterioration and the rate of deterioration based on the actually measured degree of deterioration agree with each other, relearning is performed by inputting a large number of teacher data in which the rate of deterioration is associated with the internal resistance in the row so that the probability of the rate of deterioration can be improved. When the estimated rate of deterioration and the actually measured rate of deterioration do not agree with each other, relearning is performed by inputting the teacher data where the actually measured rate of deterioration is associated with the internal resistance.)
in the step (D), a portion of the time series data of the input parameters and the degradation indicator that belongs to a verification period subsequent to the training period is treated as verification data, Input data is distinguished as data for estimation (verification) and includes internal resistance as an input parameter, as stated previously ((Mizoguchi, ¶196) " A vector having the same number of components as the number of nodes in the input layer is given as input data to the learning model 146 (input data for learning and input data for estimation). The learned input data includes at least the internal resistance at the reached SOC ( estimated SOC). The input data may include, besides the internal resistance, at least one of an internal resistance in a fully charged state, an open circuit voltage, a discharge capacity, a discharge voltage (an estimated value of the discharge capacity based on the discharge voltage), and a temperature obtained by a temperature sensor 7."). Estimation of degrees of deterioration based on internal resistances occurs on a learned model, thereby indicating that verification is subsequent to training ((Mizoguchi, ¶266) " FIG. 19 is an explanatory table illustrating the configuration of a learning model (learned) 149 according to an embodiment 5. A neural network in which the plurality of internal resistances in the time series are inputted and the degrees of deterioration in the plurality of points of time in the future are outputted is constructed by performing learning based on teacher data in which internal resistances at a plurality of points of time in the time series are used as problem data and the degrees of deterioration in the plurality of points of time in the future are used as answer data."). and the optimal exponent is found by evaluating a prediction accuracy of the battery model trained using the verification data. The learning model is retrained based on the evaluation of prediction accuracy, wherein the model may be trained on estimations (verification data) that have been found to be in agreeance with actual measured values ((Mizoguchi, ¶224) " The control unit enables relearning of the learning model 146 based on the rate of deterioration estimated using the learning model 146 and the rate of deterioration obtained by actual measurement so that the reliability of the estimation of the rate of deterioration is improved. The actually measured degree of deterioration is obtained in the predetermined row of the use history DB 35, and when the estimated rate of deterioration and the rate of deterioration based on the actually measured degree of deterioration agree with each other, relearning is performed by inputting a large number of teacher data in which the rate of deterioration is associated with the internal resistance in the row so that the probability of the rate of deterioration can be improved. When the estimated rate of deterioration and the actually measured rate of deterioration do not agree with each other, relearning is performed by inputting the teacher data where the actually measured rate of deterioration is associated with the internal resistance. "). The internal resistance curve (as an optimal exponent value, as described previously) is determined using a selected learned learning model, wherein the learned model has been optimized for accuracy as stated above ((Mizoguchi, ¶218) " The control unit 11 selects the learning model 146 corresponding to the estimated SOC and inputs the internal resistance to the learning model 146 (S144). ")
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2- 5 and 8-12 are rejected under 35 U.S.C. 103 as being unpatentable over Mizoguchi as applied to claim 1 above, and further in view of Wenjun (CN112816880A), hereinafter referred to as Wenjun.
Regarding claim 2, Mizoguchi discloses The battery degradation prediction device according to claim 1, as stated previously. Mizoguchi does not explicitly disclose the battery model in terms of being defined as a linear regression model. However, Wenjun discloses wherein the battery model is a linear regression model expressing the objective variable as a linear function of the explanatory variables. The attenuation rate of battery storage capacity is described as the battery storage capacity ((Wenjun, ¶60-62) "It should be understood that the preset battery storage capacity attenuation model is as follows: Qreten = 1-Btz Among them, B represents the attenuation rate of the battery storage capacity attenuation model, z is the first fitting parameter, and t is the storage time; Qreten represents the capacity retention rate, and the capacity retention rate refers to the charge and discharge of the tested sample battery (or the battery whose life needs to be predicted) The percentage ratio of the battery capacity after multiple cycles to the initial battery capacity. Assuming that Q0 represents the initial battery capacity and Qi represents the battery capacity after multiple cycles of charging and discharging, the first fitting parameter z can be obtained after the fitting process."). The attenuation rate (objective) is described as a function of independent variable (explanatory), wherein the relationship corresponds to a linear regression model ((Wenjun, ¶66) "It should be understood that the first decay rate in each sample data is the dependent variable of the multiple linear regression model, and the elements in each sample data correspond to the independent variables in the multiple linear regression model. Therefore, there will be multiple second decay rates parameters..")
Mizoguchi and Wenjun are both analogous to the claimed invention because they are related to the same field of endeavor of battery degradation modeling. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have modified the learning model methodology disclosed by Mizoguchi to incorporate particularly the linear regression model of the battery as disclosed by Wenjun because some teaching, suggestion, or motivation in the prior art would have led one having ordinary skill in the art to do so in order to arrive at the claimed invention. Mizoguchi discloses using a learning model to derive a relationship between input data and a predicted rate of deterioration and suggests that the learning model can incorporate deep learning, other neural networks, and other machine learning approaches ((Mizoguchi, ¶194) "The learning model 146 is a learning model expected to be used as a program module that is a part of artificial intelligence software. The learning model 146 can use a multilayer neural network (deep learning). For example, the learning model 146 can use a convolutional neural network (CNN). However, the learning model 146 may also use other neural networks. Other machine learnings
may be used. The control unit 11 operates in such a manner that the control unit 11 applies an arithmetic operation to an internal resistance inputted to an input layer of the learning model 146 in accordance with an instruction from the learning model 146, and outputs the rate of deterioration and the probability of the rate of deterioration as a determination result. In the case where the learning model 146 uses CNN, an intermediate layer includes a convolution layer, a pooling layer and a fully connected layer. The number of nodes (neurons) is also not limited to the number adopted in the case illustrated in FIG. 12"). Wenjun demonstrates using a multivariate linear regression model to characterize a linear relationship of battery parameters to the attenuation rate of the battery capacity and describes the model as having higher accuracy compared to traditional calculation methods ((Wenjun, ¶12) "According to the above-mentioned embodiments of the present invention, there is at least the following beneficial effect: by inputting the related parameters of the battery to be tested into the multiple linear regression model, the second related parameters related to the storage temperature, state of charge, and related parameters of the battery to be tested can be obtained. Attenuation rate, that is, the second attenuation rate of the battery under test includes the influence of storage temperature on the state of charge, so the second attenuation rate of the battery under test obtained through the multiple linear regression model is more accurate than traditional calculation methods; When the second decay rate of the battery to be tested is input into the battery storage capacity decay model, a more accurate relationship between the capacity retention rate of the battery to be tested and the storage time can be obtained, thereby improving the accuracy of the prediction of battery life"). Accordingly, because Mizoguchi suggested that other machine learning approaches may be used in the methodology for batter life prediction and Wenjun demonstrates high accuracy in battery life prediction with the multivariate linear regression model and one having skill in the art would recognize linear regression modeling as a simple and interpretable way to model data, it would have accordingly been obvious to combine the prior art references.
Regarding claim 3, Mizoguchi discloses The battery degradation prediction device according to claim 1, as stated previously. Mizoguchi further discloses (except the limitations surrounded by brackets ([[..]])) wherein the usage history parameters include current [[factor parameters that treat the]] current of the battery [[as a factor]], voltage [[factor parameters that treat the]] voltage of the battery [[as a factor]], and temperature [[factor parameters that treat the]] temperature of the battery [[as a factor.]] The history data (usage history parameters) includes a current, a voltage, and a temperature ((Mizoguchi, ¶240-242) "The control unit 21 acquires a current, a voltage, a SOC and a temperature from the history data 24, and transmits the acquired current, voltage, SOC and temperature to the deterioration estimation device 1 (S252). The control unit 11 receives the current, the voltage, the SOC, and the temperature (S152). The control unit 11 inputs the current, the voltage, the SOC, and the temperature to the learning model 147 (S153)."). The history data and other inputs that can be derived from the history data are used as input values to the learning model, thereby indicating that each parameter acts as an influence the output of the model ((Mizoguchi, ¶196) "The input data may include, besides the internal resistance, at least one of an internal resistance in a fully charged state, an open circuit voltage, a discharge capacity, a discharge voltage (an estimated value of the discharge capacity based on the discharge voltage), and a temperature obtained by a temperature sensor 7.");
Mizoguchi does not explicitly disclose the usage history parameters in terms of their quantifiable influence to the output of the learning model but rather just expresses the parameters as the raw values, or values derived from raw values (such as internal resistance derived from voltage and current). However, Wenjun discloses modeling independent variables of a multiple linear regression model in terms of respective coefficient values as factors of the model factor parameters that treat the * as a factor ((Wenjun, ¶103) " It should be understood that, assuming that the first attenuation rate of the sample data is Bi, the storage temperature is Ti, and the value of SOC is Soci, then the logarithm of the first attenuation rate is lnBi, and the logarithm of the reciprocal of the storage temperature is lnSoci, The related parameters are the second fitting parameters β0, β1, β2, and β3 obtained through the multiple linear regression model. "); ((Wenjun, ¶141) " At this time, through multiple linear regression model fitting, the second fitting parameters β0, β1, β2, and β3 are obtained. ")
Mizoguchi and Wenjun are both analogous to the claimed invention because they are related to the same field of endeavor of battery degradation modeling. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have modified the learning model methodology disclosed by Mizoguchi to incorporate particularly the linear regression model of the battery as disclosed by Wenjun because some teaching, suggestion, or motivation in the prior art would have led one having ordinary skill in the art to do so in order to arrive at the claimed invention. Mizoguchi discloses using a learning model to derive a relationship between input data and a predicted rate of deterioration and suggests that the learning model can incorporate deep learning, other neural networks, and other machine learning approaches ((Mizoguchi, ¶194) "The learning model 146 is a learning model expected to be used as a program module that is a part of artificial intelligence software. The learning model 146 can use a multilayer neural network (deep learning). For example, the learning model 146 can use a convolutional neural network (CNN). However, the learning model 146 may also use other neural networks. Other machine learnings may be used. The control unit 11 operates in such a manner that the control unit 11 applies an arithmetic operation to an internal resistance inputted to an input layer of the learning model 146 in accordance with an instruction from the learning model 146, and outputs the rate of deterioration and the probability of the rate of deterioration as a determination result. In the case where the learning model 146 uses CNN, an intermediate layer includes a convolution layer, a pooling layer and a fully connected layer. The number of nodes (neurons) is also not limited to the number adopted in the case illustrated in FIG. 12"). Wenjun demonstrates using a multivariate linear regression model to characterize a linear relationship of battery parameters to the attenuation rate of the battery capacity and describes the model as having higher accuracy compared to traditional calculation methods ((Wenjun, ¶12) "According to the embodiment of the present invention, at least has the following beneficial effects: by inputting the associated parameter of the battery to be tested into the multi-element linear regression model, so as to obtain the storage temperature of the battery to be tested; charge state and associated parameter related to the second attenuation rate, namely the second attenuation rate of the battery to be tested comprises the influence of the storage temperature on the state of charge; therefore, obtaining the second attenuation rate of the battery to be tested by the multi-element linear regression model is more accurate than the traditional calculation mode; At this time, the second attenuation rate of the battery to be tested is input into the battery storage capacity attenuation model, which can obtain more accurate relation of capacity retention rate and storage time of the battery to be tested, so as to improve the accuracy of prediction of battery life"). Accordingly, because Mizoguchi suggested that other machine learning approaches may be used in the methodology for batter life prediction and Wenjun demonstrates high accuracy in battery life prediction with the multivariate linear regression model and one having skill in the art would recognize linear regression modeling as a simple and interpretable way to model data, particularly in terms of the influence of parameters that can be directly measured such as current, temperature, and voltage, it would have accordingly been obvious to combine the prior art references.
Regarding claim 4, the proposed combination discloses The battery degradation prediction device according to claim 3, as stated previously. The proposed combination in further view of Wenjun discloses wherein the step (D) comprises determining the optimal exponent with a common value for the current factor parameters, the voltage factor parameters, and the temperature factor parameters. An average exponent value corresponding to battery storage capacity attenuation model is used as a first fitting parameter, wherein the average accounts for the influence of all independent variables of the linear regression model ((Wenjun, ¶97-99) " Step S230: Obtain the average power value of the set of fitting power values after removing the abnormal fitting power value, and set the average power value as the first fitting parameter of the battery storage capacity attenuation model. It should be noted that the average power value is obtained by accumulating all power values in the set of fitting power values after removing the abnormal fitting power value. Therefore, the abnormal fitting power value is removed by the box plot, and the effective fitting power value is averaged to obtain the average power value, so that the battery storage capacity attenuation model obtained by using the average power value is calculated The capacity retention rate taken is closer to the real situation. ")
Regarding claim 5, the proposed combination discloses The battery degradation prediction device according to claim 3, as stated previously. The proposed combination in further view of Wenjun discloses wherein the step (D) comprises determining the optimal exponent with independent values for the current factor parameters, the voltage factor parameters, and the temperature factor parameters. Multiple decay rates/ fitting parameters are determined for the independent variables of the multiple linear regression model ((Wenjun, ¶66) " It should be understood that the first decay rate in each sample data is the dependent variable of the multiple linear regression model, and the elements in each sample data correspond to the independent variables in the multiple linear regression model. Therefore, there will be multiple second decay rates.合 parameters. "); ((Wenjun, ¶102-104) " Step S420: Perform multiple linear regression fitting processing on the logarithm of the first decay rate, the reciprocal of the storage temperature, the logarithm of the SOC, and the associated parameters of all sample data through the preset multiple linear regression model to obtain the second fitting parameter . It should be understood that, assuming that the first attenuation rate of the sample data is Bi, the storage temperature is Ti, and the value of SOC is Soci, then the logarithm of the first attenuation rate is lnBi, and the logarithm of the reciprocal of the storage temperature is lnSoci, The related parameters are the second fitting parameters β0, β1, β2, and β3 obtained through the multiple linear regression model. Therefore, the storage temperature, SOC, related parameters, and first decay rate of each sample data in the sample data set can be fitted by a multiple linear regression model to obtain a better fitting effect. ")
Regarding claim 8, Mizoguchi discloses (except the limitations surrounded by brackets ([[..]])) A battery degradation prediction device that calculates a predicted value of a degradation indicator for a battery according to a battery model that treats powers of a plurality of usage history parameters defined on a basis of time series data about a current, a voltage, and a temperature of the battery as explanatory variables and treats the predicted value of the degradation indicator for the battery as an objective variable, the battery degradation prediction device comprising: A deterioration estimation device is disclosed that outputs the estimated life of a lead acid battery ((Mizoguchi, ¶75) " the deterioration estimation device described above, in a case where a current or a voltage when discharging is performed until the lead-acid battery or the lead-acid battery module reaches a predetermined SOC is inputted to the deterioration estimation device, the first estimation unit may input the acquired current or voltage to a learning model that outputs the rate of deterioration so as to estimate the rate of deterioration of the lead-acid battery or the lead-acid battery module."). The estimation device leverages a learning model to intake temperature, current, and voltage data to estimate the state of health of the battery ((Mizoguchi, ¶196) " The input data may include, besides the internal resistance, at least one of an internal resistance in a fully charged state, an open circuit voltage, a discharge capacity, a discharge voltage (an estimated value of the discharge capacity based on the discharge voltage), and a temperature obtained by a temperature sensor 7."). The parameters are fitted to an exponential curve to estimate the behavior of the battery (See Fig 2) and ((Mizoguchi, ¶289-290) " The control unit 11 estimates a time-series transition of the internal resistance (internal resistance curve), and stores the estimated transition of the internal resistance in the relationship DB 144 (S185). The control unit 11 derives an internal resistance curve using a method such as linear approximation or curve approximation based on the internal resistance derived this time and a plurality of internal resistances derived in the past. The past internal resistance curves may be stored in the relationship DB 144 based on the data of the deterioration history DB 142, and the internal resistance curve in the current estimation may be derived with reference to the past internal resistance curves. The control unit 11 estimates a time-series transition of the internal resistance (internal resistance curve), and stores the estimated transition of the internal resistance in the relationship DB 144 (S185). The control unit 11 derives an internal resistance curve using a method such as linear approximation or curve approximation based on the internal resistance derived this time and a plurality of internal resistances derived in the past. The past internal resistance curves may be stored in the relationship DB 144 based on the data of the deterioration history DB 142, and the internal resistance curve in the current estimation may be derived with reference to the past internal resistance curves. deterioration curve in the use history DB 143 (S186). The control unit 11 estimates the second degree-of-deterioration curve based on the internal resistance curve estimated this time and the first degree-of-deterioration curve (the relationship between the degree of deterioration and the internal resistance) stored in the relationship DB 144. ")
a computer configured to: ((Mizoguchi, ¶97) " The control units 11 may be constituted of a central processing unit (CPU), a read only memory (ROM), a random access memory (RAM), and the like. The control unit 11 may include a graphics processing unit (GPU). The control unit 11 may use a quantum computer. ")
acquire time series data about a current, a voltage, and a temperature of a prediction target battery; ((Mizoguchi, ¶170) "The control unit 21 acquires a current and a voltage from the history data 24, and transmits the acquired current and voltage to the deterioration estimation device 1 (S222)."); ((Mizoguchi, ¶240) "The control unit 21 acquires a current, a voltage, a SOC and a temperature from the history data 24, and transmits the acquired current, voltage, SOC and temperature to the deterioration estimation device 1 (S252).")
calculate the usage history parameters on a basis of the time series data; Internal resistance is calculated and stored in a use history database ((Mizoguchi, ¶172) "The control unit 11 derives the internal resistance, and stores the internal resistance in the use history DB 143 (S123).") See figure 20 depicting that the derivation of internal resistance relies upon the received current and voltage data, thereby indicating that the internal resistance is determined on the basis of such historical data. Internal resistance is also described as being derived using equations 1-3, which leverage voltage values at different time periods. ((Mizoguchi, ¶51) " The first internal resistance R is derived from the following equation (I) in a case where the first internal resistance R is paused after discharging. equation (1) "); ((Mizoguchi, ¶55) " The second internal resistance R is derived from the following equation (2) in a case where the battery is charged after a pause. equation (2) "); ((Mizoguchi, ¶60) " A point of time that discharging finishes immediately before charging starts. Accordingly, the internal resistance is calculated by the same equation as the first internal resistance or the second internal resistance. That is, in this case, the internal resistance R is derived by the following equation (3). equation (3) "). The use history database contains data for each of a plurality of state of charge values (SOC) ((Mizoguchi, ¶107) "The use history DB 143 stores a number column, an internal resistance column consisting of a first internal resistance column, a second internal resistance column, and a third internal resistance column, and a degree-of-deterioration column for each of a plurality of SOC ( estimated SOC) for each battery 3.")
generate input parameters by raising the usage history parameters by an optimal exponent; and Derived internal resistance (which are stored in the usage history database as stated previously) is used with a first degree of deterioration curve to acquire a plurality of degrees of deterioration. The degrees of deterioration are used as input to the learning model, thereby indicating that this data is being used as input parameters. The degree of deterioration curve is selected based off the correlation to the derived internal resistance value, as an optimal match to the measurements of the controlled battery ((Mizoguchi, ¶257-260) "The control unit 11 estimates the degree of deterioration based on the derived internal resistance, and stores the degree of deterioration in the use history DB 143 (S164). The control unit 11 reads the relationship DB 144, and estimates the degree of deterioration with reference to the first degree-of-deterioration curve based on the derived internal resistance. The control unit 11 acquires a plurality of degrees of deterioration (S165). The control unit 11 inputs a plurality of degrees of deterioration in time series to the learned learning model 148 and acquires a plurality of future degrees of deterioration (S166). The control unit 11 estimates the transition of the degrees of deterioration in time series (second degree-ofdeterioration curve) as described above based on the plurality of degrees of deterioration in the past, at the present, and in the future, and stores the estimated transition of the degrees of deterioration in the relationship DB 144 (S167)."). The first degree of deterioration curve is depicted as an exponential curve ((Mizoguchi, ¶17) "FIG. 2 is a graph illustrating an example of a first degree-of-deterioration curve."). The internal resistance curve can be quantified as a transition value that characterizes the curve, which would be understood to be an exponent value based on the curve’s behavior ((Mizoguchi, ¶174) "The control unit 11 estimates a time-series transition of the internal resistance (internal resistance curve), and stores the estimated transition in the relationship DB 144 (S125)."). To determine future degrees of deterioration, the input values would be mathematically correlated to continue to the trajectory of the degree of deterioration and therefore the input parameters would be understood to be raised by the exponent that characterizes the behavior so as to produce the output.
calculate the predicted value of the degradation indicator for the prediction target battery by inputting the input parameters into the battery model as explanatory variables, wherein The plurality of degrees of deterioration, derived as input parameters as stated above, are input into a learning model characterizing behavior of the battery, wherein input data to a neural network is understood to be the explanatory variables that dictate the output of the model. The learning model is further used to estimate a second degree of deterioration curve that is used to estimate the life of the battery which is then displayed on the display panel of the battery control device as the indicator of degradation ((Mizoguchi, ¶259-263) " The control unit 11 inputs a plurality of degrees of deterioration in time series to the learned learning model 148 and acquires a plurality of future degrees of deterioration (S166). The control unit 11 estimates the transition of the degrees of deterioration in time series (second degree-of-deterioration curve) as described above based on the plurality of degrees of deterioration in the past, at the present, and in the future, and stores the estimated transition of the degrees of deterioration in the relationship DB 144 (S167). The control unit 11 estimates the life (S168). The control unit 11 acquires the time ta when the degree of deterioration becomes a threshold a as the life (an exchange time). The control unit 11 transmits the second degree-of- deterioration curve and the life to the battery control device 2 (S169), and finishes the processing. The control unit 21 receives the second degree-of-deterioration curve and the life (S263), displays the second degree-of-deterioration curve and the life on the display panel 25 (S264), and finishes the processing. ")
[[the battery model is a linear regression model expressing the objective variable as a linear function of a plurality of the explanatory variables, and]]
the usage history parameters include current [[factor parameters that treat the]] current of the battery [[as a factor]], voltage [[factor parameters that treat the]] voltage of the battery [[as a factor]], and temperature [[factor parameters that treat the]] temperature of the battery [[as a factor.]] The history data (usage history parameters) includes a current, a voltage, and a temperature ((Mizoguchi, ¶240-242) "The control unit 21 acquires a current, a voltage, a SOC and a temperature from the history data 24, and transmits the acquired current, voltage, SOC and temperature to the deterioration estimation device 1 (S252). The control unit 11 receives the current, the voltage, the SOC, and the temperature (S152). The control unit 11 inputs the current, the voltage, the SOC, and the temperature to the learning model 147 (S153)."). The history data and other inputs that can be derived from the history data are used as input values to the learning model, thereby indicating that each parameter acts as an influence the output of the model ((Mizoguchi, ¶196) "The input data may include, besides the internal resistance, at least one of an internal resistance in a fully charged state, an open circuit voltage, a discharge capacity, a discharge voltage (an estimated value of the discharge capacity based on the discharge voltage), and a temperature obtained by a temperature sensor 7.");
Mizoguchi does not explicitly disclose the battery model in terms of being defined as a linear regression model. However, Wenjun discloses wherein the battery model is a linear regression model expressing the objective variable as a linear function of the explanatory variables. The attenuation rate of battery storage capacity is described as the battery storage capacity ((Wenjun, ¶60-62) "It should be understood that the preset battery storage capacity attenuation model is as follows: Q= 1-Btz wherein, B represents the attenuation rate of battery storage capacity attenuation model, z is the first fitting parameter, t is the storage time; Qreten represents capacity retention rate; capacity retention rate refers to the percentage ratio of the battery capacity to the initial battery capacity after multiple cycles are charged by the tested sample battery (or the battery to be predicted); supposing Q0 represents the initial battery capacity; Qi represents the battery capacity after charging and discharging a plurality of cycles, The first fitting parameter z can be obtained by fitting processing. "). The attenuation rate (objective) is described as a function of independent variable (explanatory), wherein the relationship corresponds to a linear regression model ((Wenjun, ¶66) "It should be understood that the first attenuation rate in each sample data is a multivariate linear regression model of the dependent variable, the element in each sample data corresponding to the multivariate linear regression model in the independent variable, therefore, there are a plurality of second fitting parameter.")
Mizoguchi does not explicitly disclose the usage history parameters in terms of their quantifiable influence to the output of the learning model but rather just expresses the parameters as the raw values, or values derived from raw values (such as internal resistance derived from voltage and current). However, Wenjun discloses modeling independent variables of a multiple linear regression model in terms of respective coefficient values as factors of the model factor parameters that treat the * as a factor ((Wenjun, ¶103) " It should be understood that, assuming that the first attenuation rate of the sample data is Bi, the storage temperature is Ti, and the value of SOC is Soci, then the logarithm of the first attenuation rate is lnBi, and the logarithm of the reciprocal of the storage temperature is lnSoci, The related parameters are the second fitting parameters β0, β1, β2, and β3 obtained through the multiple linear regression model. "); ((Wenjun, ¶141) " At this time, through multiple linear regression model fitting, the second fitting parameters β0, β1, β2, and β3 are obtained. ")
Mizoguchi and Wenjun are both analogous to the claimed invention because they are related to the same field of endeavor of battery degradation modeling. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have modified the learning model methodology disclosed by Mizoguchi to incorporate particularly the linear regression model of the battery as disclosed by Wenjun because some teaching, suggestion, or motivation in the prior art would have led one having ordinary skill in the art to do so in order to arrive at the claimed invention. Mizoguchi discloses using a learning model to derive a relationship between input data and a predicted rate of deterioration and suggests that the learning model can incorporate deep learning, other neural networks, and other machine learning approaches ((Mizoguchi, ¶194) "The learning model 146 is a learning model expected to be used as a program module that is a part of artificial intelligence software. The learning model 146 can use a multilayer neural network (deep learning). For example, the learning model 146 can use a convolutional neural network (CNN). However, the learning model 146 may also use other neural networks. Other machine learnings may be used. The control unit 11 operates in such a manner that the control unit 11 applies an arithmetic operation to an internal resistance inputted to an input layer of the learning model 146 in accordance with an instruction from the learning model 146, and outputs the rate of deterioration and the probability of the rate of deterioration as a determination result. In the case where the learning model 146 uses CNN, an intermediate layer includes a convolution layer, a pooling layer and a fully connected layer. The number of nodes (neurons) is also not limited to the number adopted in the case illustrated in FIG. 12"). Wenjun demonstrates using a multivariate linear regression model to characterize a linear relationship of battery parameters to the attenuation rate of the battery capacity and describes the model as having higher accuracy compared to traditional calculation methods ((Wenjun, ¶12) "According to the embodiment of the present invention, at least has the following beneficial effects: by inputting the associated parameter of the battery to be tested into the multi-element linear regression model, so as to obtain the storage temperature of the battery to be tested; charge state and associated parameter related to the second attenuation rate, namely the second attenuation rate of the battery to be tested comprises the influence of the storage temperature on the state of charge; therefore, obtaining the second attenuation rate of the battery to be tested by the multi-element linear regression model is more accurate than the traditional calculation mode; At this time, the second attenuation rate of the battery to be tested is input into the battery storage capacity attenuation model, which can obtain more accurate relation of capacity retention rate and storage time of the battery to be tested, so as to improve the accuracy of prediction of battery life"). Accordingly, because Mizoguchi suggested that other machine learning approaches may be used in the methodology for batter life prediction and Wenjun demonstrates high accuracy in battery life prediction with the multivariate linear regression model and one having skill in the art would recognize linear regression modeling as a simple and interpretable way to model data, particularly in terms of the influence of parameters that can be directly measured such as current, temperature, and voltage, it would have accordingly been obvious to combine the prior art references.
Regarding claim 10, the proposed combination discloses The battery degradation prediction device according to claim 8, as stated previously. The proposed combination in further view of Wenjun discloses wherein the computer is further configured to generate input parameters by raising the current factor parameters, the voltage factor parameters. and the temperature factor parameters by the optimal exponent set to a common value for each An average exponent value corresponding to battery storage capacity attenuation model is used as a first fitting parameter, wherein the average accounts for the influence of all independent variables of the linear regression model ((Wenjun, ¶97-99) " Step S230: Obtain the average power value of the set of fitting power values after removing the abnormal fitting power value, and set the average power value as the first fitting parameter of the battery storage capacity attenuation model. It should be noted that the average power value is obtained by accumulating all power values in the set of fitting power values after removing the abnormal fitting power value. Therefore, the abnormal fitting power value is removed by the box plot, and the effective fitting power value is averaged to obtain the average power value, so that the battery storage capacity attenuation model obtained by using the average power value is calculated The capacity retention rate taken is closer to the real situation. ")
Regarding claim 11, the proposed combination discloses The battery degradation prediction device according to claim 8, as stated previously. The proposed combination further in view of Wenjun discloses wherein the computer is further configured to generate input parameters by raising the current factor parameters, the voltage factor parameters, and the temperature factor parameters by the optimal exponent set to independent values for each Multiple decay rates/ fitting parameters are determined for the independent variables of the multiple linear regression model ((Wenjun, ¶66) " It should be understood that the first decay rate in each sample data is the dependent variable of the multiple linear regression model, and the elements in each sample data correspond to the independent variables in the multiple linear regression model. Therefore, there will be multiple second decay rates.合 parameters. "); ((Wenjun, ¶102-104) " Step S420: Perform multiple linear regression fitting processing on the logarithm of the first decay rate, the reciprocal of the storage temperature, the logarithm of the SOC, and the associated parameters of all sample data through the preset multiple linear regression model to obtain the second fitting parameter . It should be understood that, assuming that the first attenuation rate of the sample data is Bi, the storage temperature is Ti, and the value of SOC is Soci, then the logarithm of the first attenuation rate is lnBi, and the logarithm of the reciprocal of the storage temperature is lnSoci, The related parameters are the second fitting parameters β0, β1, β2, and β3 obtained through the multiple linear regression model. Therefore, the storage temperature, SOC, related parameters, and first decay rate of each sample data in the sample data set can be fitted by a multiple linear regression model to obtain a better fitting effect. ")
Regarding claim 12, the proposed combination discloses The battery degradation prediction device according to claim 8, as stated previously. The proposed combination in further view of Mizoguchi discloses wherein a value of the optimal exponent is set within a range from 0 to 1. Mizoguchi explicitly states that the conductance is the reciprocal of the resistance ((Mizoguchi, ¶72) " The rate of deterioration can also be estimated using conductance that is a reciprocal of resistance and is measured by a battery tester or the like."). Accordingly, by applying the reciprocal values of the internal resistance in the plot of Figure 2 as opposed to the internal resistance, one having skill in the art would expect to see a generally exponentially decaying plot, wherein a decay function is understood to be characterized by an exponent between the values of 0 and 1.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over the proposed combination of Mizoguchi and Wenjun as applied to claim 8 above, and further in view of Song et al (CN105353313A), hereinafter referred to as Song.
Regarding claim 9, the proposed combination discloses The battery degradation prediction device according to claim 8, as stated previously. The proposed combination does not disclose; however, the proposed combination in view of Song discloses wherein the current factor parameters are integral values of product of the current of the battery and time, A current factor coefficient is described as the integral of the current with respect to time ((Song, ¶43) "∫(i)*η*d(t) represents the change in battery charge and discharge capacity calculated by the energy integral, and η is the correction amount for current measurement error"); ((Song, ¶28) "The current factor coefficient Kc is obtained by using a preset current factor coefficient algorithm based on the difference between the integral of the current output current with respect to time and the integral of the output current with respect to time when calculating the SOC in the previous calculation"); ((Song, ¶118) "The current factor coefficient acquisition module 22 is used to obtain the current factor coefficient Kc for the current time based on the difference between the integral of the current output current of the battery with respect to time and the integral of the output current with respect to time when calculating the SOC in the previous time, through a preset current factor coefficient algorithm. The current factor coefficient algorithm can be Kc＝△I*△I*0.0015, where △I＝∫(Inow)*d(t)-∫(Iocv)*d(t), which represents the error value between the current DC and the current corresponding to the OCV curve, and has a positive or negative sign, while Inow represents the current and Iocv represents the current corresponding to the OCV curve") voltage factor parameters are integral values of time spent within predetermined voltages ranges, A voltage factor coefficient is described as being based on the current battery voltage and the voltage at a previous time for an SOC calculation, wherein the SOC calculation is made from an integral value ((Song, ¶117) "The voltage factor coefficient acquisition module 21 is used to obtain the current voltage factor coefficient Kv based on the difference between the current battery voltage and the voltage at the time of the previous SOC calculation, and the corrected OCV curve, through a preset voltage factor coefficient algorithm. The voltage factor coefficient algorithm can be Kv (ABS(△OCVsocnow,△OCVsocb)+ABS(Vnow,Vsocb))/100; where △OCVsocnow＝ (Vsocnow-Vsocnow-1)/(SOCnow-SOCnow-1), representing the voltage change corresponding to a 1% change in the current SOC; and SOCnow represents the current SOC, SOCnow-1 represents the SOC at the previous point in the OCV curve corresponding to the current SOC, Vsocnow represents the voltage at the previous point in the OCV curve corresponding to the current SOC, and Vsocnow-1 represents the voltage at the previous point in the OCV curve corresponding to the current SOC; △OCVsocb＝(Vsocb-Vsocb-1)/(SOCb-SOCb-1); represents the voltage change corresponding to a 1% change in the estimated SOC; SOCb represents the estimated SOC, SOCb-1 represents the SOC of the previous point on the OCV curve corresponding to the estimated SOC, Vsocb represents the voltage of the estimated SOC on the OCV curve, Vsocb-1 represents the voltage of the previous point on the OCV curve corresponding to the estimated SOC; Vnow represents the current voltage value; Vsocb represents the voltage of the estimated SOC on the OCV curve."); ((Song, ¶35) "Based on the integral of the current output current of the battery over time ∫(i)*d(t), the theoretical state of charge SOCa of the battery at the current time, and the full charge capacity Q(i) of the battery at the previous time, the current state of charge SOCb of the battery is obtained by the coulomb Ah method.") and temperature factor parameters are integral values of time spent within predetermined temperature ranges. The temperature factor coefficient is described as the integral value of the difference between the current temperature and the temperature of the previous calculation ((Song, ¶96) "The above temperature factor coefficient algorithm can be Kt＝(∫(Tnow)*d(t)-∫(Tbef)*d(t))/ (Tnow-Tocv), where Tnow represents the current temperature, Tbef represents the temperature at the time of the last calculation, and Tocv is the temperature corresponding to the OCV curve."); ((Song, ¶119) "The temperature factor coefficient acquisition module 23 is used to obtain the current temperature factor coefficient Kt based on the difference between the integral of the battery temperature over time in the current calculation and the integral of the battery temperature over time in the previous SOC calculation, using a preset temperature factor coefficient algorithm. The temperature factor coefficient algorithm can be Kt＝(∫(Tnow)*d(t)-∫(Tbef)*d (t))/(Tnow-Tocv), where Tnow represents the current temperature, Tbef represents the temperature in the previous calculation, and Tocv is the temperature corresponding to the OCV curve"); ((Song, ¶30) "The temperature factor coefficient Kt for the current calculation is obtained by using a preset temperature factor coefficient algorithm based on the difference between the integral of the battery temperature heat over time at the current battery temperature and the integral of the battery temperature heat over time at the previous SOC calculation.")
Song is analogous to the claimed invention because it is related to the same field of endeavor of battery health monitoring devices using modeling. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have leveraged the integral values as disclosed by Song as the particular factor parameters used in the multiple linear regression model disclosed by Wenjun in the proposed combination because some teaching, suggestion, or motivation would have led one having ordinary skill in the art to do so in order to arrive at the claimed invention. The linear regression model disclosed by Wenjun is given in terms of the state of charge, as given per equation described in Wenjun ¶103:
PNG
media_image2.png
51
285
media_image2.png
Greyscale
. Song states that the SOC cannot be directly measured but must be estimated by parameters which may be influenced by factors such as battery aging, ambient temperature changes, and battery charging/discharging status ((Song, ¶5) "The state of charge (SOC) of a secondary battery cannot be directly measured by instruments. It can only be estimated by parameters such as battery terminal voltage, charging and discharging current, and internal resistance. These parameters are also affected by various uncertain factors such as battery aging, changes in ambient temperature, and battery charging and discharging status."). Song demonstrates an approach to estimate the SOC by accounting for the the influence of current, temperature, and voltage as factors in the estimation, wherein the factors are derived per their integral values as stated above. By estimating the SOC values in the multiple linear regression equation disclosed by Wenjun using the estimation technique disclosed by Song, one having skill would arrive at the claimed invention. Accordingly, the combination would have been obvious so as to achieve the accurate SOC estimation.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILY GORMAN LEATHERS whose telephone number is (571)272-1880. The examiner can normally be reached Monday-Friday, 9:00 am-5:00 pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMERSON PUENTE can be reached at (571) 272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.G.L./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187